Management by Baseball

What do Hall of Fame baseball managers like Connie Mack & John McGraw have in common with today's business leaders? Why are baseball managers better role models for management than corporate heroes like Jack Welch, Jamie Dimon & Bill Gates? And just what does Peter Drucker have to do with Oriole ex-manager Earl Weaver?
Management consultant & ex-baseball reporter Jeff Angus shows you almost everything you need to know about management you can learn from Baseball.

Monday, August 01, 2011

Clarke's Second Law of New
Technology (paraphrased):For every human capability a technology creates,
it disables what exists; this may net out as progress or retardation.

In Baseball (and Beyond Baseball), new technologies that create the
capability to do things we've never been able to do before (say, Field
F/X,
which can measure infinitesimal speed and trajectory and rotational measures for
a batted ball) tend to add to human knowledge and "ability".

New technologies that merely make it easier to do the things we can already
do (broom-->vacuum cleaner, for example, or texting instead of voice
telephone), change the things we do in foreseeable and unforeseen ways, and they
don't always represent net progress. Author Arthur C. Clarke wrote a
classic sci-fi story that illustrates this counter-intuitive reality from a
military/industrial perspective.

There's a great Management By Baseball example that happened recently that
makes this effect, what I call The Peavy Principle, very easy to understand.

CHICAGO — The answer to how the
Washington Nationals would achieve their latest win seemed to reveal itself
in the second inning Saturday afternoon. Chicago White Sox starter John Danks
walked off the field, a strained muscle having ended his day after six
batters. The Nationals could feast on Chicago’s bullpen and chalk up
another win. Just the usual.

“I thought we got a break,” interim
manager John McLaren said. “I thought we were going to hit their bullpen.”

But after spending two weeks convincing
themselves they can’t lose, the Nationals lost to the White Sox, 3-0,
before 23,008 at U.S. Cellular field, just their second defeat in 14 games.
The Nationals managed two hits and struck out 11 times over 7-1/3 innings
against the White Sox’ bullpen, which received a dominant cameo by veteran
ace Jake Peavy, making the first relief appearance of his career. {SNIP}

Peavy dominated for four innings,
allowing a single and no walks while striking out seven.

Before each series, the Nationals’
hitters gather in a small room adjacent to their clubhouse. With hitting
coach Rick Eckstein, they watch video and study tendencies of each starter
they will face and the relievers. Peavy, who started Wednesday for Chicago,
fit neither category. “I didn’t see Peavy’s name on that list,”
McLaren said.

Though the Nationals never mentioned Peavy
in their hitters’ meeting, they still gave credit to his pitching. “The
bottom line is, we just didn’t swing the bats well today,” third baseman
Jerry Hairston said.

In the last fifteen years (depending on which team, a little earlier or a
little later), video library software has given coaches the ability to create,
with just a few hours of assembly by the coach or other team aides, wonderfully
organized and informative video tutorials on how opponents play, their biases
and tendencies and quirks and tells. This new technology replaces a prior,
non-technological way of doing it; word-of-mouth verbal sharing of information,
three-ring binder collections of data points, exchanged tips in the batting cage
before the game, exchanged tips in the dugout during the game.

It's not that none of that pre-video library software information
happens; it just has become secondary, delivers less impact than the new way
and, therefore, becomes relatively devalued by most of the participants. By
making a high-tech system THE WAY to get 'er done, the other ways seem to be
"old fashioned" or lower yield.

But for every ability an augmenting technology increases, it undermines an
existing capability. By being able to hyper-focus intense information about the
White Sox relievers, that attention gets invested, and so a resting starter,
Jake Peavy, who comes into a game as a reliever, is glossed over in the chosen
techno-path to success. ¿Did the Nationals have three-ring binder back-up? I'm
not sure; when interim Manager McLaren skippered in Seattle, I saw him carrying
two. But as a recently-appointed interim, he would work with the protocols the
team had already worked out. Even if they did have it, the batters would
have already tapped their cognitive investment in other, more fluidly-acquired
accustomed ways of getting data. They cannot have helped but instinctively
valued the video information over the old-fashioned.

In Baseball (an endeavour much
more brutally zero-sum competitive than the easier work domain you manage in)
a Jedi Master of finding a cognitive edge like White Sox manager Ozzie
Guillen, undoubtedly knew there was some tiny (not giant) advantage in
putting on the mound an unscheduled reliever, but in Baseball, because
it's zero-sum, tiny advantages loom large. The reality that Jake Peavy is a
monster pitcher when he's healthy had to have been a consideration as well. And
Guillen is as prepared as any manager in any field; he and pitching
coach Don Cooper would always, every game have a Plan B for who would
come in early in a still-close game if the starter is injured or blown out.

In Baseball, technology that replaces manual + verbal methods may enable
people to do what they did before faster or cheaper, but it makes the knowledge
more brittle, less hands-on, more shallowly textured. Technology eats some of
the nuance while spitting out better volume...what I call The Peavy Principle.

THE PEAVY PRINCIPLE BEYOND BASEBALL...
...is actually quite wide-spread. The most wide-spread example is cell phones
replacing land-lines. It's not technology that gives us unprecedented abilities,
but does augment the span of places we can use a telephone or type messages or
play games or execute frozen pork-bellies futures contracts. Mobile gives us
mobility, the capability to call from most anywhere (unless you're a Sprint user
in suburban Chicago or an AT&T victim in San Francisco). But the quality of
communication goes down as the degraded fidelity eliminates audible intonation
and voice affects. Is the trade-off worthwhile? For most users, I suspect the
answer is probably yes, but for communications that require clarity (business,
romance, intelligence), the loss is palpable and costly.

I'll give you a concrete example from my own practice. I was one of the
earliest users of project management software, but I didn't learn on it. Before
there was software, PMs worked with a surprising range of physical tools. I used
mechanical pencil on graph paper and, of course, had to do trial and
error, making copious use of the Eberhard
Faber Eraser Stick (known in the trade as a "poodle penis"). I
wouldn't describe this as "the good old days"; it was truly
challenging, and I welcomed my SuperProject
and later my TimeLine
(a now-gone package that was at least 4x as productive as anything on the market
today). I was project director on a USEPA contract that had 23 people who through
the project worked asynchronously in 10 cities...a massive logistic effort that
additionally required a lot of knowledge about the individual talents (no two of
whom had the same strengths and weaknesses) and concurrently was an attempt to
prove to the agency that a co-op could deliver comparable quality at lower cost.

Setting up a project was faster this way than it is using even good project
management software. Recalculating in software is much faster than
erasing/rebuilding-by-trial-and-error. Getting the first draft done is much
faster in software. But woe to the software-only solution when the plan veers
away from the original plan enough that it requires resequencing, or re-applying
the individual talents of non-commodity labor from one sequence to another.
Because project management software "believes" people are commodities,
and it's almost impossible to program human interdependencies or stored
knowledge into the database that sequences decisions.

I could actually do this significantly faster by hand. So can most
professionals who did or do it by hand, because the physical drawing and erasing
of lines, not delegating that to a machine, gives the PM a much stronger and
more textured understanding of the interdependencies.

People who learned on software (most contemporary PMPs) and at the same time
never do it by hand tend to undervalue the aspects of project management that
the software is counter-productive for or simply doesn't do. Most
learned-it-using-software suffer from The Peavy Principle, that is, they can do
it fast, but by delegating the knowledge to a
technology, they can overlook
details the technology ignores, filtering out valuable information simply
because the software developer didn't value it, or because it was costly or
perhaps impossible to embody in software.

By stuffing the data into a digital container, removed from the visible and
manipulable world of physical artifacts, they master technology, but undermine
the fullness of their craft -- as my associate Athena explained to me when she
was taking a PMP certification course, they were teaching people how to operate
software, do effective data entry and report their results thoroughly, not how
to manage projects.

I'll give you a another equally-painful example, in case you have no
experience with project management. Handling data.

Many of us who analyse data for a living actually comb through the raw data
before we start analysing it. It's time-consuming, and doesn't always have big
rewards, but we find that the exploration gives us a better handle on it and
makes it easier to track the exceptions that indicate valuable insights or dirty
data. Some of our peers, though, trust data enough to make it an unseen artifact
that's hidden in a digital container. Even when they use other software to flag
exceptions or pinpoint certain kinds of out-of-scope points, they can miss
subtle flaws that the technology helper wasn't programmed to recognize.

Even famous and brilliant scientists who don't respect the data (the noun,
the reason for the analysis) as much as the tool used to analyse it (the verb)
are missing a key piece of the grammar of data analysis. A few years ago I read
a serious clever baseball researcher's article on platoon splits (the ability,
for example, of a right-handed batter to hit left-handed pitching overall better
than right handed), and he had come to the conclusion that it was not a skill
(not a repeatable event, but one driven by luck or other external factors). His
results were quite unequivocal.

I was surprised but interested, because platoon splits are a piece of
unquestioned protocol and I love to question the unquestioned protocol. I'd
fiddled with this problem before without coming to useful conclusions, and he
had taken a very different tack in the analysis and had compiled a great swathe
of data. I asked him if he would give me a copy of the data to work with, and he
generously shared it.

I opened the file with great anticipation and started combing through the
individual rows, associating codes with the players they referred to, their
seasons, all artifacts I'd never had the pleasure of examining as consolidated
numbers. But you can imagine how disappointed I was when I saw that much of the
data was flawed, the result of a bad transform routine, one that repeated two of
the fields every so many rows (not all rows, not all fields, but regularly
making false certain rows in a predictable sequence). Each one of these rows was
within scope, and every one, taken alone, was feasible. There were no out of
scope characters or out of scope row lengths -- it was a giant pile of broken
data that smelled fresh to data cleaning routines, and turned the research
conclusions from significant to not. Only a human eye and brain considering
patterns would detect the underlying errors.

I did send the deck back to the mathematician with a note, and thanked him.
His push back was that I must be mistaken and that errors would have been caught
by his technology. He had evolved out of being a scientist and into a technology
midwife. If he ever opened his file and looked at it line by line (honestly, an
exhausting task), he would have known. The technology that enabled him to adsorb
vast piles of data and clean and analyse it and deliver insight by a thousand
slices had left him exposed to intellectual death by a thousand cuts. It had
enabled vast quantity while degrading critical quality.

What technologies do you use that threaten to impose the Peavy Principle on
your efforts? If Baseball, the most productive and accountable user of
technology can get screwed up by the Peavy Principle, I'm telling you it can
mess you up, too.